PathME: pathway based multi-modal sparse autoencoders for clustering of patient-level multi-omics data

Background Recent years have witnessed an increasing interest in multi-omics data, because these data allow for better understanding complex diseases such as cancer on a molecular system level. In addition, multi-omics data increase the chance to robustly identify molecular patient sub-groups and he...

Full description

Saved in:

Bibliographic Details
Published in:	BMC bioinformatics Vol. 21; no. 1; pp. 146 - 20
Main Authors:	Lemsara, Amina, Ouadfel, Salima, Fröhlich, Holger
Format:	Journal Article
Language:	English
Published:	London BioMed Central 16.04.2020 BioMed Central Ltd Springer Nature B.V BMC
Subjects:	Algorithms Analysis Bias Bioinformatics Biomedical and Life Sciences Cancer Clustering Computational Biology/Bioinformatics Computer Appl. in Life Sciences Deep learning Deoxyribonucleic acid DNA DNA methylation Gene expression Generalized linear models Genetic research Learning algorithms Life Sciences Machine learning Machine Learning and Artificial Intelligence in Bioinformatics Machine learning for computational and systems biology Medical treatment Methodology Methodology Article Methods Methylation Microarrays miRNA Multi-omics Mutation Noise reduction Patient clustering Patients Ribonucleic acid RNA Sensory integration Sparsity Subgroups Therapeutics Deep learning Multi-omics Patient clustering
ISSN:	1471-2105, 1471-2105
Online Access:	Get full text
Tags:	Add Tag No Tags, Be the first to tag this record!

Be the first to leave a comment!